In 2001, the first author of this paper reported that schizophrenia is associated with an increased frequency of the haptoglobin (Hp)-2 gene. The precursor of Hp-2 is zonulin, a molecule that affects intercellular tight junction integrity. Recently, we reported increased plasma IgA/IgM responses to Gram-negative bacteria in deficit schizophrenia indicating leaky gut and gut dysbiosis. The current study was performed to examine the integrity of the paracellular (tight and adherens junctions) and transcellular (cytoskeletal proteins) pathways in deficit versus non-deficit schizophrenia. We measured IgM responses to zonulin, occludin, E-cadherin, talin, actin and vinculin in association with IgA responses to Gram-negative bacteria, CCL-11, IgA responses to tryptophan catabolites (TRYCATs), immune activation and IgM to malondialdehyde (MDA) and NO-cysteinyl in 78 schizophrenia patients and 40 controls. We found that the ratio of IgM to zonulin + occudin / talin + actin + viculin (PARA/TRANS) was significantly greater in deficit than in non-deficit schizophrenia and higher in schizophrenia than controls and was significantly associated with increased IgA responses to Gram-negative bacteria. IgM responses to zonulin were positively associated with schizophrenia (versus controls), while IgM to occludin was significantly associated with deficit schizophrenia (versus non-deficit schizophrenia and controls). A large part of the variance (90.8%) in negative and PHEM (psychosis, hostility, excitation and mannerism) symptoms was explained by PARA/TRANS ratio, IgA to Gram-negative bacteria, IgM to E-cadherin and malondialdehyde (MDA) and memory dysfunctions, while 53.3% of the variance in the latter was explained by PARA/TRANS ratio, IgA to Gram-negative bacteria, CCL-11, TRYCATs and immune activation. The results show an upregulated paracellular pathway with breakdown of the tight and ahherens junctions and increased bacterial translocation in deficit schizophrenia. These dysfunctions in the intestinal paracellular route together with lowered natural IgM, immune activation and production of CCL-11 and TRYCATs contribute to the phenomenology of deficit schizophrenia.

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved super-resolution model which involves the self-attention augmented WGAN is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the HR results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we therefore design a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features, this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.

A 47-year-old physician suddenly noticed a persistent difficulty maintaining attention. He was awake, alert, and oriented. After two hours he developed fever, ageusia, and anosmia. He denied any previous history of psychiatric illness and was hydrated at the time of the subjective attention impairment. On admission, the patient remained oriented. He reported the persistence of attention problems, anosmia, and mild fatigue. The oxygen saturation 99% while he was breathing ambient air. Laboratory tests were unremarkable. A high-resolution computed tomography of the chest was normal. Nasopharyngeal and throat swabs specimens on reverse transcription-polymerase chain reaction analysis tested positive for SARS-CoV2. On illness day 3, the examination was unchanged, but he continued to complain of difficulties to stay focused. Then, he performed an objective attention test. The test demonstrated a moderate attentional impairment. On day 6, the patient reported a subjective worse in his concentration and performed a second test. Although his physical examination remained normal, the attention performance was worse as compared to day 3. Eight hours after worsening of attention impairment, the patient’s oxygen saturation dropped to 94%. From illness days 9 to 14, the patient evolved with clinical improvement. On day 10, a third objective attention test indicated a mild deficit. On day 16, he did not report any other symptom and the attention test was completely normal. Then, the patient returned to work. Neurological symptoms had been previously described in COVID- 19 patients. However, no previous research had investigated early cognitive deficits preceding the traditional symptoms.

This paper is an interdisciplinary study of novel applications of techniques and tools of an area of brain science, known as Synesthesia (involving associations and/or confusion between distinct senses), to area of Computer Science known as Immersive Virtual Reality (VR), that makes the subject’s awareness of physical self be diminished by being surrounded in an engrossing artificial environment. Natural Synesthesia has for the last decade been an important emerging area in brain science but is present in only a small proportion of the population. For example a person with Natural Synesthesia, when viewing a grapheme, may perceive a color additionally to be associated to the grapheme. In contrast, Artificial synesthesia (also known as virtual synesthesia or synthetic synesthesia) has been defined as the sensory joining due a cross-modal mapping device, where information of one sense is accompanied by an induced perception in another sense. In particular, we propose use of a multimodal manner of displaying information in VR to increase and concentrate attention. Artificial Synesthesia to synthetically create induced associations between senses, allowing Artificial Synesthesia to be experienced by anyone using a VR system. The paper describes the enhancement of immersive VR by use of Artificial Synesthesia to improve the system’s performance at steering and directing the attention of the user. We describe techniques for an enhanced immersive VR that displays associations between a variety of senses: between colors and characters, also between colors and sounds, and between sounds and the position of tactile sensations. The sense association provided by Artificial Synesthesia allows the system to better capture the user’s attention and better direct that attention. A major application of our work in VR-induced Artificial Synesthesia is to provide an enhanced methodology for controlling the attention of the subject, and to improve the direction of attention of subjects undergoing guided imagery therapies for pain relief. Other potential high-impact applications include improved immersive VR, more programmable human/computer interfaces and other medical therapies.

Schizoaffective disorder and its classification have always been a topic of discrepancy among researchers. The overlapping presentations of schizoaffective disorder, schizophrenia and psychotic bipolar disorder makes it difficult to classify and diagnose. This study is a narrative review of literatures and genetic and epidemiological research evidences, done to determine the similarities and differences between schizoaffective and psychotic bipolar disorder. Beginning from the introduction of schizoaffective disorder as "schizoaffective psychosis" by Kasanin in 1933, various researchers have proposed different views regarding the diagnosis and classifications for schizoaffective disorder. DSM-III considered schizoaffective disorder as a diagnosis of exclusion as it fails to fit the definite diagnostic criteria of schizophrenia, major affective disorders, or schizophreniform disorder. DSM-V updated the criteria of schizoaffective disorder and stated it as a life time illness rather than being an episodic disorder as in DSM-IV and hence is more reliable. However, some studies show that schizoaffective disorder patients are more like schizophrenia than mood disorder. It still remains unclear whether schizoaffective disorder is a heterogenous disease, a form of schizophrenia, a form of mood disorder or a midpoint in the continuation spectrum of schizophrenia and psychotic bipolar disease.

Regulated deficit irrigation (RDI) strategies aim to improve water usage without reducing yield. Generally, irrigation strategy effectiveness is measured as fruit yield, with little consideration of fruit quality. As water deficit and increased plant cell sclerification often associated, this study explores the effect of RDI on pear fruit stone cells, a crucial factor affecting flesh texture. The presence, distribution and development of pear fruit stone cells under RDI and full irrigation are compared using Pyrus communis L. cv. Barlett trees, employing recently developed microscope image analysis technology. The Control treatment was maintained under non-stress conditions, while the RDI treatment received an average of 15% of Control water during the latter part of Stage I fruit development. Observations at the end of Stage I and at harvest showed no effect on stone cell presence under the tested RDI strategy. Relative area of stone cells within the flesh was greater at Stage I than at harvest, as stone cell expansion occurred early in development while the (unsclerified) parenchyma cells, dominant component of the fruit flesh, expanded until harvest. Stone cell cluster density was higher near the fruit core than in the cortex center and exterior. These initial results suggest that well-planned RDI strategies will generally not affect pear fruit stone cell content and thus, textural quality. Microscope image analysis supported results from previously used analytical techniques, principally chemical, while providing a tool for better understanding the process and timing of stone cell differentiation and thus testing the factors involved.

Aboriginal Australians have a fundamental human right to opportunities that lead to healthy and flourishing lives. While the impact of trauma on Aboriginal Australians is well-documented, a pervasive deficit narrative that focuses on problems and pathology persists in research and policy discourse. This narrative risks further exacerbating Aboriginal disadvantage, through a focus on ‘fixing what is wrong’ with Aboriginal Australians, and the internalising of these narratives by Aboriginal Australians. While a growing body of research adopts strength-based models, limited research has sought to explore Aboriginal flourishing. This conceptual paper seeks to contribute to a burgeoning paradigm shift in Aboriginal research, seeking to understand what can be learned from Aboriginal people who flourish, how we best determine this, and in what contexts this can be impactful. Within, we argue the case for a new approach to exploring Aboriginal wellbeing that integrates salutogenic, positive psychology concepts with complex systems theory to understand and promote Aboriginal wellbeing and flourishing, While deeper work may be required to establish the parameters of a strength-based, culturally aligned Aboriginal conceptualisation of positive psychology, we suggest the integration of Aboriginal and Western methodologies offer unique and potent means of shifting the dial on seemingly intractable problems.

Every subset S of a Cartesian spaces Rd, endowed with differen- tial structure C∞(S) generated by restrictions to S of functions in C∞(Rd), has a canonical partition M(S) by manifolds, which are or- bits of the family X(S) of all derivations of C∞(S) that generate local one-parameter groups of local diffeomorphisms of S. This partition satisfies the frontier condition, Whitney’s conditions A and B. If M(S) is locally finite, then it satisfies all definitions of stratification of S. This result extends to Hausdorff locally Euclidean differential spaces.

In recent years, there has been significant growth in the ubiquity and popularity of three dimensional(3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution. The method consists of two main branches: the first branch com-putes attention masks for each point, while the second branch employs adaptive graph convolu-tion to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often include noise, occluded regions, weakly textured areas and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of the 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and nonnegative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder-decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain the accuracy that 1.115 pixel in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.

Cloud detection in remote sensing images is a crucial preprocessing step that efficiently identifies and extracts cloud-covered areas within the images, ensuring the precision and reliability of subsequent analyses and applications. Given the diversity of clouds and the intricacies of the surface, distinguishing the boundaries between thin clouds and the underlying surface is a major challenge in cloud detection. To address these challenges, an advanced cloud detection method, CloudformerV3, is presented in this paper. The proposed method employs a multi-scale adapter to incorporate dark and bright channel prior information into the model's backbone, enhancing the model's ability to capture prior information and multi-scale details from remote sensing images. Additionally, multi-level large window attention is utilized, enabling high-resolution feature maps and low-resolution feature maps to mutually focus and subsequently merge during the resolution recovery phase. This facilitates the establishment of connections between different levels of feature maps and offers comprehensive contextual information for the model's decoder. Experimental results on the GF1_WHU dataset demonstrate that the method introduced in this paper exhibits superior detection accuracy when compared to state-of-the-art cloud detection models. Furthermore, enhanced detection performance is achieved along cloud edges and with respect to thin clouds, showcasing the efficacy of the proposed method.

The Na+/K+ ATPase is a protein involved in the active transport of ions across the cellular membrane. Ouabain is a cardiotonic glycoside that, by inhibiting the Na+/K+ pump, interferes with cell processes mediated directly by the pump, but also indirectly influences other cellular processes, such as cellular cycle and proliferation, growth, cell differentiation, angiogenesis, migration, adhesion and invasion. We used the SK-BR-3 breast cancer cell line, mesenchymal stem cells (MSCs), and tumor-associated fibroblasts (TAFs) in vitro for determining the changes induced by Ouabain in concentration of 10-5 M, 10-6 M, 10-8 M, 10-9 M on these cellular types, employing specific methods: SEM for morphological evaluation; immunocytochemistry, flowcytometry to assess immunophenotypical markers expression, cell cycle and Annexin V/PI assay for cellular proliferation and viability testing; flowchamber assay to evaluate functional adhesion behavior; RT-PCR for Na+/K+ ATPase subunits molecular expression. Being indicative of the pro-apoptotic and inhibitory effect of Ouabain on tumor invasion and metastasis, the results support the addition of Ouabain to the oncological therapeutic arsenal, trailing the “repurposing drugs” approach.

Keywords: Innovation ecosystems, Economic sustainability, Hydrocarbon-based economies, Economic diversification, Qualitative approach

Intrinsic disorder accounts for the flexibility of protein loops, molecular building blocks that are largely responsible for the processes and molecular functions of the living world. While loops likely represent early structural forms that served as intermediates in the emergence of protein structural domains, their origin and evolution remains poorly understood. Here, we conduct a phylogenomic survey of disorder in loop prototypes sourced from the ArchDB classification. Tracing prototypes associated with protein fold families along an evolutionary chronology revealed ancient prototypes tended to be more disordered than their derived counterparts, with ordered prototypes developing later in evolution. This highlights the central evolutionary role of disorder and flexibility. While mean disorder increased with time, a minority of ordered prototypes exist that emerged early in evolutionary history, possibly driven by the need to preserve specific molecular functions. We also revealed percolation of evolutionary constraints from higher to lower levels of organization. Percolation resulted in trade-offs between flexibility and rigidity that impacted prototype structure and geometry. Our findings provide a deep evolutionary view of the link between structure, disorder, flexibility and function, as well as insights into the evolutionary role of intrinsic disorder in loops and their contribution to protein structure and function.

Enhancement in chemisorption is one of the active research areas in carbon materials. To remedy the thermally degraded chemisorption at high temperatures, we report a comprehensive study of kink structures in free-standing monoatomic carbon nanowires upon heating. Our Monte Carlo simulation considers multi-monoatomic carbon chains laterally interacted by the Van der Waals force. Our study reveals that the carbon nanowires maintain their linearity regardless of chain length at low temperatures, but this is not the case at high temperatures. Disordered kink structure is observed in the short carbon chains, especially above the Peierls transition temperature. The severe kink structure might increase the possibility of attaching negatively charged atoms, thereby contributing to the development of next-generation materials for chemisorption at high temperatures. We have also provided an important inspection that any physical property of the finite-length carbon chain predicted by ab-initio calculation should reconsider the atomic rearrangement due to the thermal instability at high temperatures.

Public concern has emerged about the effects of endocrine disruptor compounds (EDCs) on neuropsychiatric disorders. Preclinical evidence suggests that exposure to EDCs is associated with the development of the major depressive disorder (MDD) and could result in neural degeneration. The interaction of EDCs with hormonal receptors is the best-described mechanism of their biological activity. However, the dysregulation of the hypothalamic-pituitary-gonadal adrenal axis has been reported and linked to neurological disorders. On the other hand, at a worldwide level and in Mexico, the incidence of MDD has recently been increasing. Of note, in Mexico, there are no clinical associations on blood levels of EDCs and the incidence of the MDD. Methodology: Thus, we quantified for the first time the serum levels of parent compounds of two bisphenols and four phthalates in patients with MDD. Results: The levels of di-ethyl-hexyl-phthalate (DEHP), butyl-benzyl-phthalate (BBP), di-n-butyl phthalate (DBP), and di-ethyl-phthalate (DEP), bisphenol A (BPA), and bisphenol S (BPS) were determined with a gas chromatograph-mass spectrometer. Results/ conclusion: We found significant differences between concentrations of BBP between controls and patients with MDD.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Speech Emotion Recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a Deep Belief Network (DBN), a simple deep neural network (SDNN), a LSTM network (LSTM), a LSTM network with the addition of an attention mechanism (LSTM-ATN), a Convolutional neural network (CNN), and a Convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact of attention mechanism to the results. Dropout and Batch Normalization techniques are also used to improve the generalization ability (prevention of overfitting) of the models as well as to speed up the training process. The Surrey Audio-Visual Expressed Emotion database (SAVEE), and the Ryerson Audio-Visual Database (RAVDESS) database were used for training and evaluation of our models. The results showed that networks with the addition of the attention mechanism did better than the others. Furthermore, they showed that CNN-ATN was the best among tested networks, achieving an accuracy of 74% for the SAVEE and 77% for the RAVDESS dataset, and exceeded existing state-of-the-art systems for the same datasets.

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and patho-logical detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse at-tention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edge features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy re-duces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation tech-niques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare profes-sionals.

The present study aims to unravel the mechanisms of salicylic acid and rehydration and the physiological benefits in pepper plants grown under water deficit. 3x4 factorial scheme adopted, with three water regimes (irrigated, dry and rehydration) and four SA concentrations, namely: 0 (control), 0.5, 1 and 1.5 mM. Evaluated leaf moisture percentage, leaf water potential, chlorophylls (a and b), carotenoids, stomatal conductance, chlorophyll a fluorescence and H2O2 concentration at different times throughout the day and water conditions (irrigated, dry and rehydration) and SA applications (without the addition of the regulator (0) and with SA addition at concentrations equal to 0.5, 1 and 1.5 mM). Exogenous SA application favored stomatal conductance responses(gs) and modified fluorescence parameters (Fm, Fo, Fm', Fo', Fv/Fm, qP, ETR and NPQ) of sweet pepper plants subjected to drought followed by rehydration. SA concentration of 0.5 mM allowed a more expressive recovery of gs indices, effective quantum yield of photosystem II, with emphasis on excess energy (E) and maximum quantum yield (FV/FM) over time in plants subjected to drought and rehydration. We discovered that the use of SA, especially in association with rehydration, by modulating the variables gs and chlorophyll a fluorescence, prevents damage to membranes and consequently mitigates water deficit in pepper plants.

Many techniques have been and are being made to find alternatives to water-saving practices. Among them, Partial root drying (PRD), one of the effective approaches, plays a major role in reducing the harmful effects of water deficit stress. An experiment was carried out using irrigation strategies [I1 (100% full irrigation “FI”), I2 (75%FI), I3 (50%FI), and I4 (PRD (50%FI)] and soil organic mulch [L0 (Zero layer organic soil mulch” control”), L1 (Single layer organic soil mulch), L2 (Two layers of organic soil mulch), and L3 (Three layers of organic soil mulch)] to inspect the impact of those treatments for increasing yield, water productivity and saving energy under arid region conditions. To meet the study's objective, two field experiments were carried out at a private farm. Our results demonstrated a general decrease in water stress and salt accumulation inside root-zone area with PRD with L3. The PRD strategy increased fruit yields by 3.7 and 7.3% and water productivity by 51.9 and 53.1% compared with I1 during 2020/2021 and 2021/2022, respectively, while reducing the applied irrigation water by 50 %. The PRD strategy showed superior results in increasing mango yield and water productivity. In general, PRD can be used as good technique to save water and energy up to 50% and enhance productivity along with using organic mulching, ultimately improving mango yield under arid climatic regions. It may prove a good adaptation strategy for current and future water shortage scenarios of climate change.

Patients with bipolar disorder (BD) experience more frequent and intense emotions in response to environmental conditions relative to healthy subjects, which leads to mood instability. The aim of this study was to investigate the time-course of emotion regulation strategies, distraction, and reappraisal, in euthymic BD patients using electroencephalography (EEG). Fourteen BD patients and 13 matched healthy controls took part in an experiment constituted of three conditions, i.e., a passive viewing of positive, negative, and neutral pictures and two regulation conditions, one with reappraisal strategy and the other with distraction strategy. Critically, ERP results indicated that during passive viewing, the Late Positive Potential (LPP) was larger in BD patients compared with healthy controls, but only for neutral pictures. During emotion regulation, LPP amplitude was reduced in distraction condition compared with viewing one, especially for negative emotions in both patients and controls. Importantly, LPP was reduced in reappraisal condition compared with passive viewing in an early time window for negative emotions and in a later time window for positive emotions in controls but not in patients. Our findings showed that the neurochronometry of emotion regulation is faster for negative than for positive emotion in controls but not in BD patients.

The study aimed to determine, through a systematic review, the relationship between Reactive Attachment Disorder (RAD) and the presence of psychopathology in children and adolescents and to determine the existence of differences about the presence of internalising and externalising psychological problems between the RAD group and groups with other disorders or with typical development. Following the PRISMA methodology, a search was carried out in the Web of Science, PubMed and Scopus databases. The search yielded 770 results, of which only 25 met the inclusion criteria. The results, indicate a relationship between the presence of RAD and/or Disinhibited social engagement disorder (DSED) with the presence of internalising and externalising problems. These difficulties are more present in children with RAD compared to children without personal difficulties, or children with DSED, children with autism, children with intellectual disabilities or children with hyperactivity. It can be concluded that the presence of RAD has negative consequences on the mental health of children and adolescents, with these being greater in the inhibited group than in the disinhibited group, and with respect to children with autism or hyperactivity.

Autism spectrum disorder (ASD) refers to the diverse range of neurodevelopmental disorders accompanying impairments in social interaction, difficulties in communication, and stereotyped or repetitive behaviors. Unlike the older term, autism, the newer term, ASD, better reflects the broad range of autistic symptoms and denotes a single diagnostic category of autism accompanied by numerous conditions. The pineal hormone melatonin is a well-known neuroprotectant and circadian entrainer. This hormone crosses the placenta and enters the fetal circulation, then conveys photoperiodic information to the fetus during pregnancy. These actions enable normal sleep patterns and circadian rhythms, followed by normal neurodevelopment. Melatonin also reduces oxidative stress, which is harmful to the central nervous system. Therefore, melatonin acts as a neuroprotectant and circadian entrainer, and may reduce the risk of neurodevelopmental disorders such as ASD.

In Schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA). This paper aims to provide a step-by-step utilization guide for the construction of pathway-phenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia. This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows a good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathway-phenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity and which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class. In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.

Color can direct visual attention to specific locations through bottom-up and top-down mechanisms. Using Continuous Flash Suppression (CFS) as way to investigate the factors that gate access to consciousness, the current study investigated whether color also directly affected the timing of conscious perception. Low or high spatial frequency (SF) gratings with different orientations were shown as targets to the non-dominant eye of human participants. CFS patterns were presented at a rate of 10Hz to the dominant eye to delay conscious perception of the targets, and participants had the task to report the target’s orientation as soon as they could see it. With low-SF targets, two types of color-based effects became evident. First, when the targets and the CFS patterns had different colors, the targets entered consciousness faster than in trials where the targets and CFS patterns had the same color. Second, when participants searched for a specific target color, targets that matched these search settings entered consciousness faster compared to conditions where the target color was irrelevant and could vary from trial to trial. Thus, the current study demonstrates that color is a central feature of human perception and leads to faster conscious perception of visual stimuli through bottom-up and top-down attentional mechanisms.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

In this study, the potential of whey protein hydrolysate (WPH) containing LDIQK peptide and treadmill exercise to prevent cognitive decline was investigated along with the neuroprotective mechanisms. Cognitive dysfunction was induced in mice by 1 mg/kg scopolamine, followed by administration of WPH at 100 and 200 mg/kg and/or treadmill exercise at 15 m/min for 30 min five days per week. Both WPH administration and treadmill exercise significantly improved memory in mice with scopolamine-induced cognitive impairment, which was attributed to several key mechanisms, including a reduction in oxidative stress based on decreased levels of reactive oxygen species and malondialdehyde in the brain tissue and an increase in acetylcholine by increasing choline acyltransferase and decreasing acetylcholine esterase levels. Exercise and WPH also exerted neuroprotective effects by inhibiting the hyperphosphorylation of tau protein, enhancing the expression of brain-derived neurotrophic factor, and inhibiting apoptosis by reducing the Bax/Bcl2 ratio in conjunction with downregulation of the mitogen-activated protein kinase pathway. Moreover, the impact of WPH and treadmill exercise extended to the gut microbiome, suggesting a potential link with cognitive improvement. These findings suggest that both WPH intake and treadmill exercise are effective strategies for mitigating cognitive impairment, providing promising avenues for treating neurodegenerative diseases.

A subset of patients with schizophrenia experience physio-somatic symptoms reminiscent of chronic fatigue and fibromyalgia. In schizophrenia, these symptoms contribute to impaired quality of life, and are strongly related to neuro-cognitive deficits, and increased IgA responses to tryptophan catabolites. Negative and PHEM (psychosis, hostility, excitation, mannerism) symptoms, psychomotor retardation (PMR) and formal thought disorders, appear to be manifestations of a single trait reflecting overall severity of schizophrenia (OSOS). In this study, 120 patients with deficit schizophrenia (DEFSCZ) and 54 healthy subjects were assessed with the FibroFatigue (FF) rating scale, and the above-mentioned symptom domains as well as neuro-cognitive tests and biomarkers were measured. In DEFSCZ, there were robust associations between the FF score and all above-mentioned symptom domains, and impairments in semantic and episodic memory and executive functions. Furthermore, the FF score loaded highly on an OSOS latent vector (LV), which showed adequate convergent validity, internal consistency reliability and predictive relevance and fitted a reflective model. Soft Independent Modelling of Class Analogy (SIMCA) showed that the FF items discriminated DEFSCZ from controls with an overall accuracy of 100%. Interleukin IL-1β, IL-1 receptor antagonist (sIL-1RA), tumour necrosis factor (TNF)-α and CCL-11 (eotaxin) explained 66.8% of the variance in the FF score and 59.4% of the variance in OSOS. In conclusion, these data show that physio-somatic symptoms are a core component of the phenomenology of DEFSCZ and are largely mediated by neurotoxic effects of activated immune pathways, including aberrations in CCL-11, IL-1β and TNF-α signalling.

This paper explores the views of a group of young men aged 16–18, studying towards a Level 1 diploma in music in the North East area of England. It uses the conceptualization of “social haunting” to focus our discussions about how Level 1 learners articulated their experiences in the context of their learning journeys and perceived marginalization. The research took place at a large Further Education (FE) college and included a series of five “research encounters” with the group of Level 1 learners. These encounters included a range of activities, resulting in a variety of data being collected, including several activity worksheets, structured and unstructured focus group interviews, and questionnaires. This paper focusses on the outcomes of a single research activity that focused on exploring the topic of marginalization. We present a discussion on social haunting as one of the emerging findings from this research encounter, outlining how the reality of the social and cultural legacy of the UK’s industrial past can shape today’s youth and their futures. We will talk about the young men’s views of marginalization, their school-to-work transitions, the formation of masculinity in post-industrial communities and the role of Further Education in it. The paper concludes by discussing some insights for practice and future research.

Loss of function of the hippocampus or frontal cortex is associated with reduced performance on memory tasks in which subjects are incidentally exposed to cues at specific places in the environment and are subsequently asked to recollect the location at which the cue was experienced. Here, we examined the roles of the rodent hippocampus and frontal cortex in cue-directed attention during encoding of memory for the location of a single incidentally experienced cue. During a spatial sensory preconditioning task, rats explored an elevated platform while an auditory cue was incidentally presented at one corner. The opposite corner acted as an unpaired control location. The rats demonstrated recollection of location by avoiding the paired corner after the auditory cue was in turn paired with shock. Damage to either the dorsal hippocampus or the frontal cortex impaired this memory ability. However, we also found that hippocampal lesions enhanced attention directed towards the cue during the encoding phase while frontal cortical lesions reduced cue-directed attention. These results suggest that the deficit in spatial sensory preconditioning caused by frontal cortical damage may be mediated by inattention to the location of cues during the latent encoding phase, while deficits following hippocampal damage must be related to other mechanisms such as generation of neural plasticity.

With more than half of U.S. adults reporting alcohol consumption the last 30 days and nearly 6% reporting heavy drinking, alcohol imposes a significant socioeconomic burden. While there are a host of current medications approved to treat alcoholism, they have limited effectiveness and lack support from both providers and patients. Semaglutide, a medication originally developed for obese type 2 diabetics, may offer a distinctly new approach. A survey to assess the impact of semaglutide on alcohol consumption and desire was designed and distributed in semaglutide-user Facebook and Reddit groups. Of those who had reported drinking before starting semaglutide, 21% of respondents (N= 121) ceased drinking altogether, and 88·4% of respondents (N=538) reported a reduction in their desire to consume alcohol after commencing their medication. Respondents also shared further personal accounts of decreased occurrences of compulsive or "addictive" behaviors beyond a reduction in the desire to consume alcohol. Considering the growing number of reports detailing experiences such as these, combined with our survey results indicating a reduction in both frequency and quantity of alcohol consumption, we strongly recommend conducting scientifically rigorous investigations and clinical trials to explore the potential of semaglutide for addressing conditions associated with alcohol use.

The passion of bodybuilding athletes for a symmetric, lean, heavily muscled body leads them to carry out exhausting exercise programs and restrictive eating regimens, sometimes resulting in disordered eating behaviors. This study investigates potential exacerbators on the development of disordered eating in bodybuilding and strength athletes. The study involved 103 Cypriot bodybuilding athletes of both sexes, performing at three levels: professional, recreational and strength athletes. The Eating Attitude Test 26 (EAT-26) and The Three Factor Eating Questionnaire (TFEQ-R21) were used to evaluate disordered eating and eating behaviors respectively. The current study was performed under the auspices of the Hellenic Center of Education & Treatment of Eating Disorders (KEADD). The degree of deviation between the perceived ideal body weight and the actual body weight was associated with increased risk of eating disorder. Athletes who desired a lower body weight recorded higher scores on EAT-26 overall, and the subscales of dieting and bulimia. Cognitive restraint and emotional eating scales of TFEQ-R21 were more pronounced in the non-professional athletes. The emotional eating score was higher in women. There is a need for appropriate sport-specific, gender-specific preventive intervention to deescalate the risk of eating disorder, in both professional and non-professional bodybuilding athletes.

Background: Post-traumatic stress disorder (PTSD) is a common debilitating psychiatric condition for which pharmacological therapy is not always solvable. Various treatments have been suggested for these patients. Deep brain stimulation (DBS) is currently under investigation for patients affected by PTSD. 2) Methods: We review the neurocircuitry and up to date clinical concepts that may be of relevance for the implementation of DBS in posttraumatic stress disorder (PTSD). 3) Results: The role of DBS in treatment-refractory PTSD patients has been investigated relying on both preclinical and clinical studies. 4) Conclusions: DBS for PTSD is in its preliminary phases and likely to provide hope to patients with medical refractory PTSD following the results of randomized controlled studies.

We will apply Pascal’s hydrostatic pressure law to black holes and also to the Hubble sphere. Haug has recently demonstrated how hydrostatic pressure in water can be utilized to determine the Planck length. One can conceptualize the energy in the Hubble sphere as a type of superfluid, and if this is correct, then Blaise Pascal’s hydrostatic pressure formula may also be applicable to such a superfluid, even within the Hubble sphere. By employing the same method on the Hubble sphere and considering it as a Schwarzschild black hole with the critical Friedmann mass, we can estimate the Planck length based on its hypothetical hydrostatic pressure.

Proteomic analysis revealed the preservation of many proteins in the “Heslington brain” (which is at least 2,600-year-old brain tissue uncovered within the skull excavated in 2008 from a pit in Heslington, Yorkshire, England). Five of these proteins (“main proteins”), heavy, medium, and light neurofilament proteins (NFH, NFM, and NFL), glial fibrillary acidic protein (GFAP), and myelin basic (MBP) protein are engaged in the formation non-amyloid protein aggregates, such as intermediate filaments and myelin sheath. We used a wide spectrum of bioinformatics tools to evaluate the prevalence of functional disorder in several related sets of proteins, such as “main proteins” and their 44 interactors, as well as all other protein identified in the Heslington brain. These analyses revealed that all five “main proteins”, half of their interactors and almost one third of the Heslington brain proteins are expected to be mostly disordered. Furthermore, most of the remaining proteins are expected to contain sizable disordered regions. This is in contrary the expected substantial (if not complete) elimination of the disordered proteins from the Heslington brain. Therefore, it seems that the intrinsic disorder of NFH, NFM, NFL, GFAP, and MBP, their interactors and many other proteins might play a crucial role in preserving the Heslington brain by forming tightly folded brain protein aggregates, in which different parts are glued together via the disorder-to-order transitions.

Abstract: The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core database of Web of Science and the knowledge graph software, this paper makes a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related literature has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change of modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

The study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, the study combines causal attention modules with residual connections, proposing the Causal Residual Attention Module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also widens the receptive field. It effectively utilizes global features, particularly capturing vertically correlated features prominently present in subgraphs of candidates. This significantly enhances its capability to model pulsar data.In the experiments, the model is trained and validated using the HTRU1 dataset. The study compares the average negative log-likelihood score with baseline models like GMM, STM, and PixelCNN. The results demonstrate the superior performance of the our model over other models. Finally, the study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from camera. For image feature extraction, the ResNet50+FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and regional voxel fusion methods. After information fusion, the Coordinate attention mechanism and SimAM attention mechanism extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm, the proposed algorithm improves the mAP value by 7.9% in BEV view and 7.8% in 3D view at IOU=0.5 (cars) and IOU=0.25 (pedestrians and cyclist). At IOU=0.7 (cars) and IOU=0.5 (pedestrians and cyclist), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

3D point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we design an augmented sampling and grouping (ASG) module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to mine point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first try to use a linearly-normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module is adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that our method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which was 0.5% higher than PCT. Our network also archived 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

Speech emotion recognition is a critical component for achieving natural human-robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral-temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to recognize emotions from the modulation-filtered cochleagram. The channel-level attention and spatial-level attention modules are used to capture emotional saliency maps of channel and spatial feature representations from the 3D convolution feature maps, respectively. Furthermore, the temporal-level attention module captures significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the IEMOCAP dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves a better unweighted accuracy of 71% in categorical emotion recognition than several existing approaches in the experiments. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

Culture exploits the acquisition of meaningful content by crafting regimes of shared attention, determining what is relevant, valuable, and salient. Culture changes the field of relevant social affordances worthy of being acted upon in a context-sensitive manner. When relevant affordances are highly weighted, their attentional capture and their salience increase the probability of them being enacted due to the associated expectation for minimizing prediction error. This process is known as active inference. In the digital era, individuals need to infer the action-related attributes of digital cues, here characterized as digital affordances. The digital affordances of digital social platforms are of particular interest here. Digital social affordances are defined as online possibilities of social interactions. By their own nature, these are salient because they are related to social interactions and relevant social cues. However, the problem of digital social platforms is that they are not equivalent to situated social interactions because their structure is built, mediated, and defined by third-parties with diverse interests. The third-parties behind the digital social platforms are using the same mechanism exploited by culture to manipulate the shared patterns of attention. Moreover, digital social platforms are deliberately designed to be hyper-stimulating, making digital social affordances highly rewarding and increasingly salient. This appropriation, for economic purposes, is an issue of great importance, especially as the COVID-19 pandemic brought deep global changes, pushing societies to an online digital way of life. Here, we examined different types of digital social affordances under an active inference view, placing them into two categories, those for self-identity formation, and those for belief-updating. This paper aims to analyze digital social affordances in light of the prediction error dynamics they might elicit to their users. Although each of the analyzed digital social affordances allows different epistemic and instrumental digital actions, they all share the characteristic of having an "easy" and a fast expected rate of error reduction. Here, we aim to provide a new hypothesis about how the design behind digital social affordances is built on our natural attractiveness to minimize prediction error and the resulting positive embodied feelings when doing so. Finally, it is suggested that because digital social affordances are becoming highly weighted in the field of affordances, this might be putting at risk our context-sensitive grip on a rich, dynamic and varied field of relevant affordances.

The object detection task is usually affected by complex backgrounds. In this paper, a new image object detection method is proposed, which can perform multi-feature selection on multi-scale feature maps. By this method, a bidirectional multi-scale feature fusion network is designed to fuse semantic features and shallow features to improve the detection effect of small objects in complex backgrounds. When the shallow features are transferred to the top layer, a bottom-up path is added to reduce the number of network layers experienced by the feature fusion network, reducing the loss of shallow features. In addition, a multi-feature selection module based on the attention mechanism is used to minimize the interference of useless information on subsequent classification and regression, allowing the network to adaptively focus on appropriate information for classification or regression to improve detection accuracy. Because the traditional five-parameter regression method has severe boundary problems when predicting objects with large aspect ratios, the proposed network treats angle prediction as a classification task. The experimental results on the DOTA dataset, the self-made DOTA-GF dataset and the HRSC 2016 dataset show that, compared with several popular object detection algorithms, the proposed method has certain advantages in detection accuracy.

Few-shot classification is challenging since the goal is to classify unlabeled samples with very few labeled samples provided. It has been shown that cross attention helps generate more discriminative features for few-shot learning. This paper extends the idea and proposes two cross attention modules, namely the cross scaled attention (CSA) and the cross aligned attention (CAA). Specifically, CSA scales different feature maps to make them better matched, and CAA adopts the principal component analysis to further align features from different images. Experiments showed that both CSA and CAA achieve consistent improvements over state-of-the-art methods on four widely used few-shot classification benchmark datasets, miniImageNet, tieredImageNet, CIFAR-FS, and CUB-200-2011, while CSA is slightly faster and CAA achieves higher accuracies.

Linum album is an important medicinal plant contains important lignan compounds such as podophyllotoxin as well as fatty acids. Despite the high medicinal value, it has not been studied in agricultural conditions so far. This study was conducted to evaluate the morphological, phenological, and physiological responses of six L. album accessions under water deficit treatments (100% available water, 75%, 50%, and 25%) in pot conditions. Based on the results the morphological properties of accessions reduced due to water deficit. Accessions of UTLA7 and UTLA9 showed higher seed yield and dry weight of the vegetative part. The occurrence of phenological stages in the accessions showed a significant difference. Maturity was accelerated in plants under stress conditions, and accession of UTLA9 completed its growth earlier than others. Physiological responses of the accessions did not have the same trend based on the measured traits, and significant differences were observed depending on the trait and accession. The most important result of this study was the diversity of responses in different accessions. The results showed that the effect of water stress on the measured traits depends on the level of stress and accession, which suggests that it is possible to select the tolerable accessions for the production of the desired product. Based on the results, plant breeders may be able to use the chlorophyll content as a marker to identify tolerate L. album accessions.

Brain-Computer Interface (BCI) systems can assist physically challenged people to interact with their surroundings and improve the quality of their lives. Decoding human thoughts is a powerful technique that can assist paralyzed people who have lost their speech production ability. Speaking is a combined process involving synchronizing the brain and the oral articulators. This paper proposed a high-accuracy brain wave pattern recognition based on inner speech using a novel feature extraction method. Only eight EEG electrodes were used in this study, and they were set on selected spots on the scalp. Support Vector Machine (SVM) was employed to decode the recorded EEG dataset into four internally spoken words which are: Up, Down, Left, and Right. The proposed approach achieved overall classification accuracy that ranges between 96.20% to 97.5%. In addition, more performance evaluation metrics were estimated to test the reliability of classifying the EEG-based inner speech data, and we obtained 97.61%, 97.50%, and 97.73% for F1-score, recall, and precision respectively. Furthermore, the Area Under Curve of the Receiver Operating Characteristic (AUC-ROC) proved the strength of the proposed approach for classifying the specified inner speech commands by achieving a macro-average amount of 99.32%. The inner speech classification method using electroencephalography proposed in this work can clinically help improve communication for patients with problems including speech disorder, mutism, cognitive development, executive function, and psychopathology.

Movement disorders can be associated with various drugs and systemic diseases, posing diagnostic challenges for clinicians. This review explores the spectrum of movement disorders associated with pharmacological agents and medical conditions. Drug-induced movement disorders encompass a broad range of conditions resulting from exposure to medications that affect several neurotransmitters. Neuroleptic medications can lead to tardive dyskinesia, characterized by involuntary, repetitive movements. Antipsychotics and antiemetics, including metoclopramide, may induce acute dystonic reactions. Other drugs, such as dopamine receptor-blocking agents, can contribute to drug-induced parkinsonism. Beyond medications, movement disorders can also arise secondary to systemic diseases. Metabolic disorders like Wilson's disease, characterized by copper accumulation, may manifest as chorea or dystonia. Inflammatory conditions such as rheumatoid arthritis and systemic lupus erythematosus can result in secondary movement disorders due to autoimmune-mediated processes affecting the central nervous system. Movement disorders linked to drugs or systemic diseases often necessitate a thorough clinical evaluation, including detailed medication histories and comprehensive systemic assessments. Neuroimaging, laboratory investigations, and, in some cases, cerebrospinal fluid analyses and electrodiagnostic studies help in establishing accurate diagnoses. Management involves addressing the underlying cause, modifying medications, and employing symptomatic treatments.

The cytosolic PNGase (peptide:N-glycanase; Png1 in yeast; NGLY1/Ngly1 in human/mice), also known as peptide-N4-(N-acetyl-beta-glucosaminyl)-asparagine ami-dase, is a well-conserved deglycosylation enzyme (EC 3.5.1.52) which catalyzes the non-lysosomal hydrolysis of an N(4)-(acetyl-β-D-glucosaminyl) asparagine residue into N-acetyl-β-D-glucosaminylamine and a peptide containing an aspartate residue. This enzyme (NGLY1) plays essential roles in clearance of misfolded or unassembled gly-coproteins through a process named ER-associated degradation (ERAD). Accumulating evidence also points out that NGLY1 deficiency can cause an autosomal recessive hu-man genetic disorder associated with abnormal development and congenital disorder of deglycosylation. In addition, the loss of NGLY1 can affect multiple cellular pathways, including but not limited to NFE2L1 pathway, Creb1/Atf1-AQP pathway, BMP path-way, AMPK pathway, and SLC12A2 ion transporter, which might be the underlying reasons for a constellation of clinical phenotypes of NGLY1 deficiency. The current comprehensive review indeed uncovers the detailed NGLY1’s structure and its im-portant roles for participation in ERAD, involvement in CDDG and potential treatment for NGLY1 deficiency.

Cannabis is the most widely cultivated, trafficked and abused illicit drug (“WHO | Cannabis,” n.d.; “World Drug Report 2020,” n.d.). In 2018, an estimated 192 million people aged 15-64 years used cannabis for nonmedical purposes globally (Degenhardt et al., 2013). The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated that, across the globe, there were more than 22·1 million people with cannabis dependence (Degenhardt et al., 2018). Moreover, the same study calculated that cannabis dependence could be accounted for 646 thousand Disability Adjusted Life Years, globally. Importantly, cannabis dependence mostly affects young adults (20-24 years), and thus has significant negative impact on the growth and productivity of not only these individuals but also to the societies and nations (Degenhardt et al., 2013). In addition to the dependence syndrome, cannabis use is associated with increased risk of psychosis, cognitive dysfunction, academic problems, and road side accidents (Volkow et al., 2014). A review showed a fairly consistent associations between cannabis use and both lower educational attainment and increased reported use of other illicit drugs (Macleod et al., 2004). In the United States, Cannabis Use Disorder (CUD) is an escalating problem in young adults by legalization (Cerdá et al., 2020) where National Survey on Drug Use and Health reported increased prevalence from 5.1% in 2015 to 5.9% in 2018 in 18-25 year olds (“2019 NSDUH Detailed Tables | CBHSQ Data,” n.d.). The psychoactive effects are due to type 1 cannabinoid receptor (CB1), the cannabinoid binding protein, that are highly expressed in the cerebellar cortex (Marcaggi, 2015). CB1 is primarily found in the molecular layer at the most abundant synapse type in the cerebellum (Marcaggi, 2015) that can shape the spike activity of cerebellar Purkinje cell (Brown et al., 2019). Moreover, granule cell to Purkinje cell synaptic transmission can trigger endocannabinoid release (Alger and Kim, 2011), which may be important for information processing by cerebellar molecular layer interneurons (Dorgans et al., 2019). This suggests that endocannabinoids could be essential to neurocognitive aspects of cerebellar function (Di Marzo et al., 2015)^,(Marcaggi, 2015)^,(Alger and Kim, 2011). Accumulating evidence also suggests cerebellar modulation of the reward circuitry and social behaviour, via direct cerebellar innervation of the ventral tegmental area (VTA) including dopamine cell bodies (A1) in the VTA (Carta et al., 2019). The VTA-dopamine (DA) signalling in the nucleus accumbens (NAc) and the medial prefrontal cortex (mPFC) (Lohani et al., 2019) play a key role in motivatedbehaviours and cognition. Cerebellar neuropathological changes can result in aberrant dopaminergic activity in the NAc and mPFC (ROGERS et al., 2011)^,(Lohani et al., 2019). Therefore, there is a critical need to determine how cerebellum modulate limbic VTA-DA signalling. Cerebellar Non-Invasive Brain Stimulation (NIBS) is postulated to be most relevant in CUD since endocannabinoids are essential to cerebellar function that includes reward-related behaviours, information processing, and cognitive control. (Di Marzo et al., 2015)^,(Marcaggi, 2015)^,(Alger and Kim, 2011). Furthermore, cerebellar NIBS can facilitate training of cognitive control in CUD during a during visual cue reactivity paradigm using a mobile virtual reality (VR) interface that can also allow remote delivery of cerebellar NIBS in conjunction with VR-based cognitive training for home-based intervention. Specifically, transcranial electrical stimulation (tES) can be translatable to low-cost (<$150) mobile devices that can be used in a low resource home-based setting (Carvalho et al., 2018).

In western societies about one in six employees works in shifts. Shiftwork is associated with a number of poor somatic and psychological health outcomes, especially sleep issues. Higher rates of absenteeism and accidents in the workplace are possible consequences. Still, prevention programs and treatment options that are specifically tailored to shift workers’ needs are rare. We devised a 4-week online sleep intervention (n = 21) and compared treatment outcomes to our outpatient treatment for shift workers (n = 12). Measures included the WHO-5, ISI, and ESS scales as well as sleep diaries. Shift workers reported worse symptoms of insomnia than other participants. Results show significant average increases in sleep efficiency (+ 7%) and total sleep time (+ 25 min.), as well as significant improvements regarding insomnia symptoms and wellbeing in the online sample. Rates of improvement did not differ between the online and outpatient samples. Sleep disorders affect a relevant part of the working population, especially shift workers. Online approaches to treatment of these issues seem feasible and effective. Randomized controlled trials are needed.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

Emotional dysfunction, including flat affect and emotional perception deficits, is a specific symptom of schizophrenia disorder. We used a modified multimodal odd-ball paradigm with fearful facial expressions accompanied by congruent and non-congruent sounds to investigate the impairment of emotional perception and reaction to other people's emotions. We analyzed subjective assessments and ERP data for emotionally charging congruent and non-congruent stimuli in patients with schizophrenia and healthy peers. The results showed the deficit of multimodal perception of fearful stimuli in patients with schizophrenia compared to healthy controls. The amplitude of N50 was significantly higher in subjects of the control group for non-congruent stimuli than congruent and did not differ in patients with schizophrenia. The dynamics of P100 and N200 components confirmed the impaired sensory gating in patients with schizophrenia. The lower amplitude of P3a could be associated with deficits in verbal memory and attention, less emotional arousal, or incorrect interpretation of emotional valence as specific features of patients. The difficulties in identifying the incoherence of facial and audial components of emotional expression could be significant in understanding the psychopathology of schizophrenia.

Dirac's Large Number Hypothesis (LNH), proposed in 1937, has captivated the scientific community with its exploration of profound correlations between cosmic and atomic scales. This hypothesis delves into the intricate interplay between the infinitesimally small and the vastly large, offering potential insights into the fundamental nature of the universe. As we continue to uncover the mysteries of the cosmos, the LNH oscillates on the precipice of scientific acceptance—neither fully validated nor entirely dismissed. In this review paper, we embark on a comprehensive journey through Dirac's LNH, shedding light on its theoretical underpinnings, its implications for universe models, and its resonance with the Anthropic Principle. We delve into the possibilities of variable gravitational constants and continuous mass creation, inviting further exploration into the intricacies of our cosmic symphony. By embracing the ongoing quest for understanding, we endeavor to unravel the profound harmonies that connect the infinitesimal with the infinite, contributing to the symphony of knowledge in theoretical physics and cosmology.

Plant growth regulators (PGRs) and deficit irrigation (DI) play a crucial strategic role in sustaining agricultural yield and reducing water stress in drought prone areas. The study was carried out in 2021-2022 to assess the impact of various PGRs (SA & NAA) & irrigation levels (60% Epan, 80 % Epan, and 100 % Epan) with DI & partial root drying (PRD) irrigation strategies. PGR application boosted fruit yields and demonstrated its significance in reducing water stress by lowering canopy temperature, maintaining higher relative water content in the leaves, modifying stomatal opening, and increasing photosynthetic rate. PGRs were used to assess the increased accumulation of total phenols, flavonoids, and improved antioxidant activity. Results indicated that exogenous use of PGRs like SA+NAA in conjunction with 80% irrigation levels increased fruit yield (31.97 and 40.38%) and water productivity (64.92 and 75.39%) for DI and PRD treatments, respectively. Thus standardizing irrigation and crop management practices including PGR application can be recommended to boost pomegranate productivity particularly in water-scarce environments.

The use of saline water under drought conditions is critical for sustainable agricultural development in arid regions. Biochar used as a soil amendment to enhance soil properties such as water-holding capacity and the source of nutrition elements of plants. Therefore, the experiment was conducted to evaluate the effects of biochar application on the morpho-physiological traits and yield of tomatoes under combined salinity and drought stress into greenhouses. There were 16 treatments consist two water quality fresh and saline (0.9 and 2.3 dS m-1), three deficit irrigation levels (DI) 80, 60, and 40% addition 100% of ETc, and biochar application by rate 5% (BC5%) (w/w) and untreated soil (BC0%). The results indicated that the salinity and water deficit negatively affected morphological, physiological, and yield traits. In contrast, the application of biochar improved all traits. The interaction between biochar and saline water leads to decreased vegetative growth indices, leaf gas exchange, relative water content of leaves (LRWC), photosynthetic pigments, and yield, especially with the water supply deficit (60 and 40% ETc), where the yield decreased by 42.48% under the highest water deficit at 40% ETc compared to the control. The addition of biochar with freshwater led to significantly increased of vegetative growth, physiological, yield, WUE, and less proline content under all various water treatments compared to untreated soil. In general, biochar combined with DI and freshwater could be improve morpho-physiological attributes, to sustain the growth of tomato plants, and increase productivity in arid and semi-arid regions.

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain makes it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Abstract Objectives To assess the causes and risk factors of posttraumatic stress disorder (PTSD) in adult asylum seekers and refugees. To explore whether the causes and risk factors of PTSD, between male and female adult refugees/ asylum seekers is different. Study design Systematic review of current literature. Data Sources PubMed, Web of Science, Scopus and Google Scholar up until February 2019 Method A structured systematic search was conducted in the relevant databases. Papers were excluded, if they failed to meet the inclusion and exclusion criteria. Afterwards, a qualitative assessment was performed on the selected papers. Results 12 Studies were included for the final analysis. All papers were either case studies/report or cross sectional studies. The number of traumatic events experienced by refugees/asylum seekers, is the most frequently reported pre-migration causes for PTSD development. Whilst acculturative stress, is the most common post migration stressor. There were mixed reports, regarding the causes of PTSD between both genders of refugees/asylum seekers. Conclusion This reviews’ findings, have potential clinical application into helping clinicians, to risk stratify refugees/asylum seekers for PTSD development and thus aid in embarking on earlier intervention measures. However, more rigorous research similar to this one, is needed for it to be implemented into clinical practice.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

To accurately identify high-quality functional wood with superior mechanical properties and enhance its durability, it is imperative to understand the relationships between the physical and mechanical attributes of diverse wood types. Presently, there exist issues related to accuracy, efficiency, and differentiation in studying these performance correlations. The Liang Kleeman (L-K) information flow theory, a causative analytical approach rooted in fundamental physics principles, offers swift computational capabilities. Furthermore, the coefficient of variation in L-K information flow quantitatively gauges the intensity of causal effects. Therefore, this article adopts a causal artificial intelligence perspective and introduces a wood performance analysis method based on L-K information flow.This study focuses on four Chinese fir varieties, including fast-growing Chinese fir (YKS), red heart Chinese fir (CSH, XXH), and iron heart Chinese fir (XXT). By computing and comparing variation coefficients of L-K information flow for various physical properties and their impact on mechanical properties, causal relationships between these properties are elucidated. These findings are then applied to predict wood mechanical properties, achieving a remarkable maximum accuracy of 90%.In the future, these findings will prove invaluable for the selection of high-quality wood and for improving the manufacturing process to prolong the service life of functionalized wood.

Simulated (sim) racing is an emerging esport that has garnered much interest in recent years and has been a relatively under-researched field in terms of expertise and performance. When examining expertise, visual attention has been of particular interest to researchers, with eye tracking technology commonly used to assess visual attention. In this study, we examined the visual attention allocation of high and low skilled sim racers during a time trial task using Tobii 3 glasses. Participants were allocated to either group according to their fastest lap times. Our results indicate that when eye tracking metrics were normalised to lap time, there was no difference in the relative length of attention allocation (fixation behaviour), although lower skilled racers did have significantly greater fixation counts and durations across laps overall. Interestingly, high and low skilled sim racers differed in where they allocated their attention during the task, with high skilled sim racers allocating significantly less attention to the track relative to other areas of the display. This would allow higher skilled racers to obtain relatively more information from heads-up display elements in-game, all whilst driving at faster speeds. This study provides evidence that high and low skilled sim racers show similar lap time-corrected fixation behaviour, but how they allocate attention while driving differs.

Many drastic actions are taken by cocaine users for the sake of experiencing high dopamine levels, which depends on iron for its synthesis. Dopamine depletion and iron deficiency are also involved in the symptoms of restless leg syndrome (RLS). The intersecting biochemical pathways of cocaine use, iron deficiency, and RLS have not been adequately investigated. This case report reveals the successful treatment of a patient with these conditions. A 63-year-old male with a history of cocaine use disorder, insomnia, and RLS sought emergency care for suicidality. Upon admission, he was also found to be iron-deficient. He revealed that his RLS worsened when he attempted to abstain from cocaine. He also used alcohol to sustain the effects of cocaine when the cost of cocaine was too high. During hospitalization, his mood, cravings, and RLS were resolved with adjunctive iron supplementation as well as 300 mg of Wellbutrin. If iron-deficient, replenishment of adequate dopaminergic receptor density and function via supplementation may play an essential role in the prevention of cocaine use and cessation of cocaine withdrawal. Further research is warranted to validate these findings and understand the implications of iron supplementation in addiction medicine.

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by persistent deficits in social interaction and communication, repetitive movements, abnormal focusing on objects, or activity that can significantly affect the quality of life of the afflicted. Brain neuronal and glial cells have been implicated. It has a genetic component but can also be triggered by environmental factors or drugs. For example, prenatal exposure to valproic acid (VPA) or acetaminophen (APAP) or ingestion of propionic acid (PPA) can increase the risk of ASD. Recently, epigenetic influences on ASD have been brought to the forefront of investigations on the etiology, prevention, and treatment of this disorder. Epigenetics refers to DNA modifications that alter gene expression without making any changes to the DNA sequence. Although an increasing number of pharmaceuticals and environmental chemicals are being implicated in the etiology of ASD, here we specifically focus on the molecular influence of the above-mentioned drugs on epigenetic alterations in neuronal and glial cells and their potential connection to ASD. It is concluded that a better understanding of these phenomena can lead to more effective interventions in ASD.

CLIPPERS which stand for Chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids, is a chronic inflammatory disorder of the central nervous system. The aetiology of CLIPPERS remains unclear, although its pathogenesis shows immune-mediated processes. The diagnosis of CLIPPERS is challenging without a specific biomarker hence, it is important to consider if both the clinical and radiological features are consistent with the diagnosis, or rather any other disease that mimics CLIPPERS. The Authors present a case of a 40-year-old Female patient diagnosed with CLIPPERS syndrome in India. Differential diagnosis, clinical and MRI characteristics of CLIPPERS syndrome, and treatment approaches are discussed.

Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain’s electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Specifically, hierarchical regression modeling was used to predict treatment response from baseline demographics, symptom severity, and resting-state EEG features in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). Across models, both age and baseline symptom severity, assessed by the Beck’s Depression Inventory, were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance (p~0.07 in multiple models). These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition, characterized by persistent deficits in communication/social interaction and restricted and repetitive patterns of behavior, interests or activities. It is usually identified in childhood, despite late diagnoses in cases of greater functionality. Havighurst proposes a set of fundamental tasks throughout the life cycle, whose achievement is probably compromised in the ASD, even though it lacks scientific evidence. This study innovates, as it is based on Havighurst's theoretical model for understanding the development process of three adults and one young person, diagnosed with ASD level I, at different life stages. Through the methodology of life histories, interviews were subject to content analysis. Results pointed out the early detection and interventions importance, minimizing adversities, above all by those who had late diagnoses. Self-perceived success, not expected, was found in some development tasks (e.g. conscience and morality development). Also were noticed expected limitations related to ASD (e.g. difficulties in relationships with peers and in the labor market inclusion). Probably, in cases of greater severity and less functionality/autonomy, there would be greater failure in development tasks. It is crucial to improve research on this developmental model and on the factors that delay the diagnosis, including cases of different levels of severity.

The etiology of autism spectrum disorder (ASD) has not yet been completely elucidated. Through time, multiple attempts have been made to uncover the causes of ASD. Different theories have been proposed, such as that it is caused by “Gods Wrath,” alternations in the gut–brain axis with an emphasis on gut dysbiosis, post vaccine complications, and genetic or even autoimmune causes. In this review we present data covering over 170 000 participants at age ranging from 2 to 14 years focusing on human cell changes rather than using animal models found in previous studies. The male/female ratio was 4:1. Data collection occurred in many countries covering ethnically diversified subjects. Moreover, we aim to show how the progress in genetic techniques influence the explanation found in medical White Papers based on the human genetic samples we have observed from our studies

TThe passion of bodybuilding athletes for a symmetric, lean, heavily muscled body leads them to carry out exhausting exercise programs and restrictive eating regimens, sometimes resulting in disordered eating behaviors. This study investigates potential exacerbators on the development of disordered eating in bodybuilding and strength athletes. The study involved 103 Cypriot bodybuilding athletes of both sexes, performing at three levels: professional, recreational and strength athletes. The Eating Attitude Test 26 (EAT-26) and The Three Factor Eating Question-naire (TFEQ-R21) were used to evaluate disordered eating and eating behaviors respectively. The current study was performed under the auspices of the Hellenic Center of Education & Treatment of Eating Disorders (KEADD). The degree of deviation between the perceived ideal body weight and the actual body weight was associated with increased risk of eating disorder. Athletes who desired a lower body weight recorded higher scores on EAT-26 overall, (p=0.001), and the subscales of dieting (p=0.01) and bulimia. (p=0.001). Cognitive restraint and emotional eating scales of TFEQ-R21 were more pronounced in the non-professional athletes. (p=0.01). The emotional eating score was higher in women. There is a need for appropriate sport-specific, gender-specific preventive intervention to deescalate the risk of eating disorder, in both profes-sional and non-professional bodybuilding athletes.

Accurate estimation of Remaining Useful Life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention based hierarchical transformer (STAR) frame-work. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrate hierarchical encoder-decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms current state-of-the-art models for RUL prediction.

People are drawn to woodcut-style designs due to their striking visual impact and strong contrast. However, traditional woodcut prints and previous computer-aided methods have not addressed the issues of dwindling design inspiration, lengthy production times, and complex adjustment procedures. We propose a novel network framework, the Woodcut-style Design Assistant Network (WDANet), to tackle these challenges. Notably, our research is the first to utilize diffusion models to streamline the woodcut-style design process. We've curated the Woodcut-62 dataset, featuring works from 62 renowned historical artists, to train WDANet in absorbing and learning the aesthetic nuances of woodcut prints, offering users a wealth of design references. Based on a noise reduction network, our dual cross-attention mechanism effectively integrates text and woodcut-style image features. This allows users to input or slightly modify a text description to quickly generate accurate, high-quality woodcut-style designs, saving time and offering flexibility. As confirmed by user studies, quantitative and qualitative analyses show that WDANet outperforms the current state-of-the-art in generating woodcut-style images and proves its value as a design aid.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of Unmanned Aerial Vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top-down features, and significant changes in vehicle scales. To address this, we propose a novel Dual Mixing Attention Network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play Dual Mixing Attention Module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of Spatial Mixing Attention (SMA) and Channel Mixing Attention (CMA). First, the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. Moreover, the DMAM can be readily inserted into backbone networks at any depth to improve vehicle discrimination. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. Our code and models will be publicly released.

With the rise of the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), an abundant amount of trajectory data has made research on location prediction more popular. The check-in data shared through LBSN hides information related to life patterns, obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices is different from check-in data that has semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose A Self-Attention for Next Location Prediction based on Semantic Mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k nearest POI, then use the reciprocal of the distance from the stay point to the k nearest POI and the number of categories as weights. Finally, we use the probability to express semantic without losing other important semantic information. Furthermore, this research combined with sequential pattern mining can bring richer semantic features. In order to better perceive the trajectory, temporal features learn periodicity of time series by sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

Several commerical quantum computers are now available that offer Hybrid Classical-Quantum computing. Application is made to a classical-quantum model of human neocortex, Statistical Mechanics of Neocortical Interactions (SMNI), which has had its applications published in many papers since 1981. However, this project only uses Classical (super-)computers. Since 2015, PATHINT, has been used as a numerical algorithm for folding path-integrals. Applications in several systems in several disciplines has generalized been from 1 dimension to N dimensions, and from classical to quantum systems, qPATHINT. Papers have applied qPATHINT to neocortical interactions and financial options. The classical space described by SMNI applies nonlinear nonequilibrium multivariate statistical mechanics to synaptic neuronal interactions, while the quantum space described by qPATHINT applies synaptic contributions from Ca2+ waves generated by astrocytes at tripartite neuron-astrocyte-neuron sites. Previous SMNI publications since 2013 have calculated the astrocyte Ca2+ wave synaptic interactions from a closed-form (analytic) expression derived by the author. However, more realistic random shocks to the Ca2+ waves from ions entering and leaving these wave packets should be included using qPATHINT between electroencephalographic (EEG) measurements which decohere the quantum wave packets. This current project extends calculations to multiple scales of interaction between classical events and expectations over the Ca2+ quantum processes to include these random shocks to fit EEG data to the SMNI model, with previous analytic forms for the quantum processes replaced by qPATHINT. The classical-quantum system is fit using the author's Adaptive Simulated Annealing (ASA) importance-sampling optimization code. Gaussian Quadratures are used for numerical calculation of momenta expectations of the astrocyte processes that contribute to SMNI synaptic interactions. This project demonstrates how some hybrid classical-quantum systems may be calculated using only classical (super-)computers. Recent calculations also are reported using the closed-form expression, with and without shocks.

Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. Multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on few sample input texts to showcase the effectiveness and interpretability of our model.

In this paper we address the understanding of the problem, of why a deep learning model decides that an individual is eligible for a loan or not. Here we propose a novel approach for inferring, which attributes matter the most, for making a decision in each specific individual case. Specifically we leverage concepts from neural attention to devise a novel feature wise attention mechanism. As we show, using real world datasets, our approach offers unique insights into the importance of various features, by producing a decision explanation for each specific loan case. At the same time, we observe that our novel mechanism, generates decisions which are much closer to the decisions generated by human experts, compared to the existent competitors.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention (QIA) in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state-of-the-art.

Water deficit (drought) is still one of the most important stress factors affecting not only yields, but also production quality. Breeders are focusing on breeding cultivars and crop lines that are more resistant to water shortages. In addition to breeding, for example, for the size of the roots and changes in the color of the leaves, focus on changing the morphotype i.e. long chaff, multirowed ear. It is predicted that wheat genotypes with a morphotype change should be more drought tolerant. Therefore, our research focused on the study of changes in the physiological parameters of wheat (cv. ´Bohemia´) with a changed morphotype (genotypes ´284-17´ [long chaff] and genotype ´29-17´ [multirowed ear]) in relation to exposure to drought. Pigment content, photosynthesis and transpiration rate, water potential were measured in plants irrigated and affected by water deficit. Water deficit was shown to have a statistically significant effect on transpiration rate, photosynthetic rate, water potential and total chlorophyll content. Furthermore, the influence of the genotype on the observed parameters and on the effect of water deficit was demonstrated. Due to the fact that cv. ´Bohemia´, increases during such periods of transpiration, so for this reason it is not suitable for dry areas. Genotype ́29-17 ́ appears promising with respect to drought resistance.

Water deficit greatly affects global crop growth and productivity, particularly in water-limited environments like upland rice cultivation, leading to reduced grain yield. Plants activate various defense mechanisms during water deficit, involving numerous genes and complex metabolic pathways. Exploring orthologous genes, linked to enhanced drought tolerance, can aid functional validation in target species using genomic data from model organisms. Here, we studied the OsCPK5 gene in upland rice, an AtCPK6 ortholog from A. thaliana, by overexpressing it in the BRSMG Curinga cultivar. Transformants were assessed under two conditions: water deficit applied 79 days after seeding, lasting 14 days, followed by 7 days of irrigation at 80% field capacity. Physiological data and samples were collected at R3, R6, and R8 stages. GM plants consistently exhibited higher OsCPK5 gene expression across stages, peaking during grain filling. During this phase, GM plants displayed reduced stomatal conductance, photosynthetic rate, and increased water use efficiency compared to non-GM plants under drought. GM plants also exhibited higher filled grain percentage in both irrigation conditions. Their drought susceptibility index was 0.9 times lower than NGM plants, and they maintained a higher chlorophyll a/b index, indicating sustained photosynthesis. NGM plants under water deficit exhibited more leaf senescence, while OsCPK5-overexpressing plants retained green leaves. Overall, OsCPK5 overexpression induced diverse drought tolerance mechanisms, hinting at potential for future development of more drought-tolerant rice cultivars.

Trauma in childhood and adolescence has long-term negative consequences in brain development and behavior and increases the risk for psychiatric disorders. Among them, post-traumatic stress disorder (PTSD) during adolescence illustrates the connection between trauma and substance misuse, as adolescents may utilize substances to cope with PTSD. Drug misuse may in turn lead to neuroadaptations in learning processes that facilitate the consolidation of traumatic memories that perpetuate PTSD. This reflects, apart from common genetic and epigenetic modifications, overlapping neurocircuitry engagement triggered by stress and drug misuse that includes structural and functional changes in limbic brain regions and the salience, default-mode, and frontoparietal networks. Effective strategies to prevent PTSD are needed to limit the negative consequences associated with the later development of a substance use disorder (SUD). In this review, we will examine the link between PTSD and SUDs, along with the resulting effects on memory, focusing on the connection between the development of a SUD in individuals who struggled with PTSD in adolescence. Neuroimaging has emerged as a powerful tool to provide insight into the brain mechanisms underlying the connection of PTSD in adolescence and the development of SUDs.

Purpose: Pitch plays an important role in auditory perception of music and language. This study provides a systematic review with meta-analysis to investigate whether individuals with autism spectrum disorder (ASD) have enhanced pitch processing ability and identify the potential factors associated with processing differences between ASD and neurotypicals. Method: We conducted a systematic search through six major electronic databases focusing on the studies that used nonspeech stimuli to provide a qualitative and quantitative assessment across existing studies on pitch perception in autism. We identified potential participant- and methodology-related moderators and conducted meta-regression analyses using mixed-effects models. Results: On the basis of 22 studies with a total of 464 participants with ASD, we obtained a small-to-medium positive effect size (0.26) in support of enhanced pitch perception in ASD.Moreover, the mean age and non-verbal IQ of participants were found to significantly moderate the between-studies heterogeneity. Conclusion: Our study provides the first meta-analysis on auditory pitch perception in ASD and demonstrates the existence of different developmental trajectories between individuals with ASD and neurotypicals. Non-verbal ability can be a significant contributor to the lower-level/ local processing bias in ASD. We highlight the need for further investigation of pitch perception in ASD under challenging listening conditions. Future neurophysiological and brain imaging research studies with a longitudinal design are also needed to better understand the nature of the atypical processing in ASD to obtain new insights into the underlying neural mechanisms and to help guide auditory-based interventions for improving language and social functioning.

Ethyl loflazepate (EL) is a benzodiazepine derivative that has been reported to activate the gustatory cortex. Our department routinely uses EL as a first-line treatment for idiopathic and psychogenic taste disorders, although little has been reported in the literature with respect to patient outcomes, so we conducted a retrospective study examining its safety and efficacy. Between 2008 and 2020, 49 patients (14 males and 35 females; mean age, 62.1 years) were diagnosed with taste disorders and received EL as their only treatment for &gt;14 days. Severity of taste disorder was evaluated using the paper disc method by Sakai et al [7], and treatment efficacy was evaluated using the Visual Analogue Scale, wherein patients gave subjective ratings for their symptoms (reductions by &gt;50% after administration of EL for 4 weeks were defined as mprovements). Results showed that the improvement rates for patients with idiopathic and psychogenic taste disorders were 55% and 70%, respectively. Additionally, the majority (78%) improved within 2 weeks, and side effects were mild (seven cases of sleepiness and one case of dizziness). We conclude that EL is an appropriate first-line medication for patients with idiopathic and psychogenic taste disorders.

There is growing recognition of the adverse effects of body image dissatisfaction (BID) and eating disorder (ED) symptoms on adolescent health. The aim of this study was to estimate the prevalence of ED symptoms, BID, and their relationship in adolescents from public schools in Southern Brazil. A total of 782 schoolchildren (male: n=420, female: n=362); age: 15 ± 0,4 years) answered a self-administrated questionnaire to identify sociodemographic data. Children´s Figure Rating Scale was adopted to identify body image and Eating Attitudes Test (EAT-26) was applied to investigate ED symptoms. Inferential statistics and hierarchical model-controlled logistic regression were used for association between variables. Most of the schoolchildren reported being satisfied with their bodies. However, we observed a higher prevalence of dissatisfaction among girls for being overweight and thinness among boys. Female students and students from schools located in the central area of the city showed higher chances of developing ED symptoms, and the absence of symptoms of ED appeared to act as a protective factor against BID in schoolchildren. Results of this study show the need to reflect on these factors that influence the development of ED and non-acceptance of their own body in a population concerned with their physical appearance.

Sugarcane plays a crucial role in global sugar and ethanol production. Conventionally, sugar-cane propagation involves planting billets. However, Brazilian researchers have introduced the innovative pre-sprouted seedlings (PSS) method, widely used in the MEIOSI (Simultaneously Occurring Interrotational Method) system. Although MPB has several advantages over the con-ventional method, its sensitivity to water scarcity is a challenge. The study aimed to evaluate the survival and growth of PSS inoculated with Bacillus licheniformis and Bacillus subtilis under differ-ent water regimes. The experiment was conducted in the field in a randomized block in strips (split-block) using a 2×4 factorial scheme consisting of two inoculation conditions (with and without bacteria) and four water regimes (0%, 33%, 66%, and 100% of the ideal irrigation). The bacteria increased the PSS's survival and water use efficiency in the field. In addition, inocula-tion increased root and shoot growth, as well as the nutrient uptake such as N, P, Mn, Zn, and Cu, which resulted in a higher number of stalks per meter and, therefore, a higher multiplica-tion rate in the MEIOSI system. Inoculation proved to be a promising alternative for establishing PSS under water restriction.

Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network LBFNet is proposed in this paper.First, a lightweight convolutional neural network LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning makes the model accuracy reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.

Aim: the main aim of this study is to compare the onset of effectiveness of mental nerve block with lingual infiltration and inferior alveolar nerve block on patients visiting dental hospital for extraction of mandibular anterior teeth. Materials and methods: The study was set in a clinical setting. This was a randomized controlled double-blind cross study, where 25 subjects received two treatments at an interval of at least 1 week in a within a dental hospital.Inferior alveolar nerve block and lingual nerve block injection is given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered for the right side. Mental nerve block along with lingual infiltration of the corresponding tooth was given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered on the left side.Visual analogue scale (VAS) was used in this study, which consists of a scale with range of 0-10 pain severity on which 0 indicated absence of pain and 10 the worst pain possible. The patients were asked to give scores corresponding to his/her pain, after IANB, mental nerve block and lingual infiltration and these scores were recorde.Results: From the results we can see that patients gave scores between 3-4, stating that they had moderate pain during extraction after administration of IANB (54%) (graph 2) and VAS scores of 0-1 were given by patients, which is suggestive of no pain during extraction after administration of Mental nerve block and lingual nerve infiltration (62%)Conclusion: Within the limits of the study we can conclude that mental nerve block along with lingual infiltration is an effective way to completely and quickly anaesthetise the central incisor to the 1st premolar region.

This review paper explores auditory tactile (AT) synesthesia, a rare neurological condition where sounds evoke tactile sensations. The paper provides a historical overview of the condition and discusses its epidemiology, with a prevalence of less than 1% of the general population. The neurological basis of AT synesthesia is explored, including the role of cross-modal processing and hyperconnectivity within the brain. The paper also describes the phenomenology of the condition, including the range of tactile sensations that can be experienced in response to different sounds. The occurrence of AT synesthesia in the present-day world is discussed, including its relationship to music and art. Various hypotheses surrounding the development and maintenance of AT synesthesia are reviewed, focusing on genetic and environmental factors. The implications for clinical practice are explored, including potential benefits for individuals with sensory processing disorders. Finally, the paper concludes with a discussion of future directions for research in this field, including the need to explore further the underlying neural mechanisms of AT synesthesia and potential therapeutic interventions.

Frontal incomes play an important role in human behavioral regulation and can be a determinant of eating behavior. The aim of this study was to analyse the frontal incomes in obese patients with and without Binge Eating Disorder (BED), compared to normoweight (NW) subjects and to analyse the effect of sex and binge disorder on quality of life, with age and BMI as covariates. A total of 114 participants comprised three different groups (obese with BED, obese without BED and NW) completed the Frontal Assessment Battery (FAB) and Impact of Weight on Quality of Life (IWQOL-lite). The results showed that obese patients, with and without BED, have poorer frontal functioning than NW persons. Obese patients with BED have lower performance in frontal income than obese patients without BED. Male participants have a higher perception of quality of life in all dimensions, with women showing lower values in self-esteem and sex life. Obese with BED show greater weaknesses in physical function. These results suggest that low frontal incomes and worse quality of life characterize obese patients, more evident in obese patients with BED.

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understand their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

Purpose of the study was to explore the association of cognition with hazardous drinking, binge drinking and alcohol use disorder in schizophrenia and schizoaffective disorder. Cognitive deficits are common in schizophrenia. Alcohol might be associated with additional cognitive impairment in schizophrenia patients. The study population included 3362 schizophrenia and schizoaffective disorder patients in Finland. Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Binge drinking was obtained from the AUDIT-C. Alcohol use disorder (AUD) diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task (5-CSRTT), or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association of alcohol use with RT test and PAL test was analyzed with log-linear regression and logistic regression, respectively. After adjustment for age, education and age at first psychotic episode, hazardous drinking in females was associated with lower median RT. Compared to never binge drinkers, male and female participants drinking 6 or more doses of alcohol monthly or less had lower median RT. In the PAL test both first trial memory score (FTMS) and total errors adjusted score (TEAS) were associated with better performance in males drinking 6 or more doses of alcohol weekly or more and in females drinking 6 or more doses monthly or less. Higher PAL TEAS was associated with AUD in females Some positive associations between alcohol and cognition were found in male and female schizophrenia and schizoaffective disorder patients with hazardous drinking and binge drinking.

We discuss the role of the opposing principles of order and disorder in physical and biological systems in determining stability, growth and evolution and bring forth the potential role of a cosmic ordering agency. We analyze its role in decreasing entropy by coarse-graining and hence in determining the initial low entropy state of the big bang universe. Since all physical and biological systems have either cycles of order and disorder alternating, or may have chaotic evolution with non-linear laws, the same is expected of the dynamics of the whole universe as well. The entropy of the initial state of the universe could be low because of the reduction of degrees of freedom (DoF) as one moves from physical encoding to neural encoding and then on to psychic encoding of information in a nested manner by coarse-graining. It is by such encoding that this cosmic agency enables the universe to pass through the big crunch phase and then rolls it out as the big bang universe from the initial state of low entropy.

Introductions- Liveable housing environments face the menace of global climate change. In-frastructure (including buildings and houses) continuously experiences significant impacts ex-acerbated by natural variability in climate. The study addressed how climate change impacts the resilience of residential buildings, increased maintenance frequency, and the wellbeing and comfort of residents in UK residential buildings. Design/Methodology/Approach: This study used deductive reasoning and an empirical epis-temological methodology as a data-collecting basis to evaluate primary information obtained via a questionnaire. Regression analysis was ultimately employed to analyse the collected da-ta. Result/Discussions: The findings show a significant relationship between climate change and the wellbeing of the occupants of UK residential buildings. Also, the results show that social wellbeing is more important to the occupants than mental and physical wellbeing. It reveals that residential buildings in the UK suffer the cost of maintenance due to continuous reduc-tion in the building fabric's resilience to the impacts of climate change; for instance, a recent increase in rainfall/storms results in unprecedented flooding, which continues to damage the UK residential building fabrics.

In order to the effect of hydrated phase change materials on suppressing coal spontaneous combustion, the thermogravimetric experiment and reaction activation energy analysis experiment were used to explore the changes of combustion characteristic, parameters characteristic temperature and activating energy of gas coal, long flame coal, meagre coal, and lean coal before and after adding hydrated phase change materials. The research results showed that hydrated phase change materials increased the coal samples’characteristic temperature point and have effective inhibitory effects on different stages of oxidation process.However, the effect was best at low temperatures, as hydrated phase change materials undergo phase change and absorb heat when heated at low temperatures, isolating coal from contact with oxygen. The activating energy increased by 1.138 KJmol-1～23.048 KJmol-1 and the mass loss reduced by 1.6%～9.3% after the inhibition of the coal sample,indicating that the oxidation rate of various coal samples can be slowed down and the spontaneous combustion can be suppressed by using hydrated phase change materials.At the same time, this material can also reduce the combustibility index of meagre coal and lean coal, as well as the comprehensive combustion index and maximum loss rate of long flame coal and gas coal.

This review explores the transformative impact of sensor technologies in early disease detection. It highlights recent advancements in nanotechnology-based sensors, particularly focusing on their applications in identifying disease biomarkers at early stages. The integration of these sensors with artificial intelligence for predictive analytics in healthcare is also discussed. Applications in cancer, infectious diseases, and chronic disease management underscore their diverse utility. Challenges in sensor technology adoption and future directions, including multiplexed sensors and wearable devices, are examined. This review underscores sensors' potential in enhancing disease management and patient outcomes.

Legumes develop root nodules that harbour endosymbiotic bacteria, rhizobia. These rhizobia convert nitrogen to ammonia by biological nitrogen fixation. A thorough understanding of the biological nitrogen fixation in legumes and its regulation is key to develop sustainable agriculture. It is well known that plant hormones affect nodule formation; however, most studies are limited to model legumes due to their suitability for in vitro, plate-based assays. Specifically, it is almost impossible to measure the effects of exogenous hormones or other additives during nodule development in crop legumes such as soybean as they have huge root system in soil. To circumvent this issue, the present research develops suitable media and growth conditions for efficient nodule development under in vitro, soil free conditions in an important legume crop, soybean. Moreover, we also evaluate the effects of all major phytohormones during soybean nodulation under identical conditions. This versatile, inexpensive, scalable and simple protocol provides several advantages over previously established methods. It is extremely time-and resource-efficient, does not require special training or equipment, and produces highly reproducible results. The approach is expandable to other large legumes as well as for other exogenous additives.

No studies have examined the immune fingerprint of major neuro-cognitive psychosis (MNP) or deficit schizophrenia using M1 macrophage cytokines in combination with chemokines such as CCL-2 and CCL-11. The present study delineated the neuro-immune fingerprint of MNP/deficit schizophrenia by analyzing plasma levels of IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2, CCL-2 and CCL-11 in MNP (n=120) versus healthy controls (n=54) in association with neurocognitive deficits (as assessed with the Brief Assessment of Cognition in Schizophrenia) and PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. All immune biomarkers were significantly higher in MNP than in normal controls. MNP was best predicted by a combination of CCL-11, TNF-α, IL-1β and sIL-1RA which yielded a bootstrapped (n=2000) area under the Receiver Operating Curve of 0.985. Composite scores reflecting M1 macrophage activity and neurotoxic potential including combined effects of CCL-11 plus CCL-2 were significantly increased in MNP. Nevertheless, the effects of increased IL-1β and TNF-α in MNP were attenuated (statistically) by increased sIL-1RA and sTNFR2, two negative immune-regulatory markers. A large part of the variance in PHEM (38.4%-52.6%) and negative (65.8-7439%) symptoms was explained by combinations of immune markers whereby CCL-11 was consistently the most important. The immune markers also explained a large part of the variance in the Mini Mental State examination, list learning, digit sequencing task, category instances, controlled word association, symbol coding and Tower of London. Soft Independent Modeling of Class Analogy performed on the biomarkers showed that the inter-class distance between the models constructed around MNP and controls was 19.3 indicating a good separation. Partial Least Squares analysis showed that 72.7% of the variance in overall phenomenology was explained by the regression on IL-1β, sIL-1RA, CCL-11, TNF-α (all positively) and education (inversely). It is concluded that the combination of the above-mentioned markers defines MNP as a distinct neuro-immune disorder and that those markers in combination explain a large part of the variance in memory and executivive impairments and PHEMN symptoms.